The ability of cells to sense and respond to signals is an essential requirement of life. Genetically encoded biosensors meet this need by detecting, for example, chemicals and triggering gene expression in response. This concept is used across the life sciences to sense molecules in basic research, diagnostics, and treatment. Crucially, biosensors can be used to isolate and engineer microbes that sustainably produce value-added chemicals from renewable starting materials and thus play a key role in the transition to a circular economy. For instance, they can be used to find the “best producers” in large pools of natural or genetically engineered microbes, which is in many cases the bottleneck in the development of new biotechnological processes and products.
However, native biosensors are usually unfit for most of the desired applications, since they do not sense the right molecules (or products) of interest and they frequently do not respond to the right concentration range. This project aims at overcoming this limitation using a data-driven engineering approach. It involves the development of novel methods to experimentally assess biosensor variants in extremely high numbers (up to hundreds of millions per experiment) at low experimental cost and effort. Furthermore, it entails the exploitation of the resulting “big data” on biosensors with cutting-edge machine learning techniques to build computer models for the design of biosensors. The overall goal of the project is the development of an integrated platform for the engineering and design of biosensors with new-to-nature properties “à la carte”. This novel, data-driven approach aims at breaking new grounds in biosensor engineering through synergies between synthetic biology and artificial intelligence paving the way to novel, sustainable bioprocesses.
